From ef5ad8e95f540f5d92078715da9e699070ecc197 Mon Sep 17 00:00:00 2001
From: MLErik <baerenjesus@gmail.com>
Date: Wed, 25 Sep 2019 18:17:26 -0400
Subject: [PATCH] updated tests to reflect changes to sparse_rail_generator
---
flatland/envs/rail_generators.py | 13 ++++++-------
tests/test_flatland_envs_sparse_rail_generator.py | 3 +--
2 files changed, 7 insertions(+), 9 deletions(-)
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index 266d2bec..de780402 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -628,8 +628,6 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
else:
connected_neighb_idx = available_nodes
- print(current_node, connected_neighb_idx)
-
# Connect to the neighboring nodes
for neighb in connected_neighb_idx:
if neighb not in node_stack and neighb in open_nodes:
@@ -660,13 +658,13 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
# Place train stations close to the node
# We currently place them uniformly distributed among all cities
built_num_trainstation = 0
- train_stations = [[] for i in range(_num_cities)]
- if _num_cities > 1:
+ train_stations = [[] for i in range(nb_nodes)]
+ if nb_nodes > 1:
for station in range(num_trainstations):
spot_found = True
reduced_node_radius = node_radius - 1
- trainstation_node = int(station / num_trainstations * _num_cities)
+ trainstation_node = int(station / num_trainstations * nb_nodes)
station_x = np.clip(
node_positions[trainstation_node][0] + np.random.randint(-reduced_node_radius, reduced_node_radius),
@@ -778,12 +776,12 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
# Slot availability in node
node_available_start = []
node_available_target = []
- for node_idx in range(_num_cities):
+ for node_idx in range(nb_nodes):
node_available_start.append(len(train_stations[node_idx]))
node_available_target.append(len(train_stations[node_idx]))
# Assign agents to slots
- for agent_idx in range(num_agents):
+ for agent_idx in range(nb_nodes):
avail_start_nodes = [idx for idx, val in enumerate(node_available_start) if val > 0]
avail_target_nodes = [idx for idx, val in enumerate(node_available_target) if val > 0]
start_node = np.random.choice(avail_start_nodes)
@@ -905,4 +903,5 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
def argsort(seq):
# http://stackoverflow.com/questions/3071415/efficient-method-to-calculate-the-rank-vector-of-a-list-in-python
return sorted(range(len(seq)), key=seq.__getitem__)
+
return generator
diff --git a/tests/test_flatland_envs_sparse_rail_generator.py b/tests/test_flatland_envs_sparse_rail_generator.py
index e1647524..fd6e7b88 100644
--- a/tests/test_flatland_envs_sparse_rail_generator.py
+++ b/tests/test_flatland_envs_sparse_rail_generator.py
@@ -743,8 +743,7 @@ def test_sparse_rail_generator_deterministic():
num_neighb=3,
# Number of connections to other cities/intersections
seed=215545, # Random seed
- grid_mode=True,
- enhance_intersection=False
+ grid_mode=True
),
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=1,
--
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